Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Post-transcriptional regulatory events are increasingly thought to play a critical role in the establishment of gene expression states. However, our current knowledge of these processes and the cellular context in which they function remains rather primitive. The proposed research combines state-of-the-art computational and experimental systems biology to reveal the vast landscape of RNA- regulatory interactions controlling the post-transcriptional fates of mammalian mRNAs. Our aims are motivated by a set of exciting recent observations by our group that RNA-regulatory elements, and the factors that bind them, have substantial impact on transcriptome dynamics, steady-state abundance of proteins, and consequent cellular phenotypes such as growth. These recent observations have been enabled by the development of a new computational framework for discovering functional RNA regulatory elements from genome-wide molecular profile data on RNA behaviors and gene expression dynamics. In particular, we have made significance advances in identification of structural RNA elements and the interacting RNA binding proteins that regulate transcript stability. The phenomenal success of these efforts have motivated us to pursue a reverse-engineering strategy for decoding RNA-regulatory interactions and characterizing their biological contributions. In this interdisciplinary proposal we describe the details of our multi-faceted strategy which includes: (1) large-scale computational analysis of genomes and global RNA-behaviors (e.g. stability) to discover functional RNA regulatory elements, en masse; and (2) in vitro and in vivo experiments to determine the RNA binding proteins that interact with these elements. As such, the proposed work promises to fundamentally advance our understanding of gene regulation and enable a synthetic biology infrastructure for programming and manipulating mammalian gene expression states and cellular phenotypes.